What is anomaly detection in finance?

What is anomaly detection in finance?

Anomaly detection in finance refers to identifying unusual patterns or outliers in financial data that may indicate fraud, errors, or significant market changes.

How does it work?

It typically involves statistical methods and machine learning algorithms to analyze historical data and flag deviations from expected behavior.

What are some common techniques used?

Common techniques include statistical tests, clustering methods, and supervised learning algorithms like decision trees and neural networks.

Can you give an example of its application?

Sure! Anomaly detection is used in credit card fraud detection, where transactions that deviate from a user's normal spending pattern are flagged for review.

What challenges are faced in implementing it?

Challenges include dealing with large volumes of data, ensuring accuracy to minimize false positives, and adapting to evolving patterns of behavior.